CN111898253B - Reservoir dispatching and downstream river ecological environment protection cooperation value evaluation method - Google Patents

Reservoir dispatching and downstream river ecological environment protection cooperation value evaluation method Download PDF

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CN111898253B
CN111898253B CN202010678306.8A CN202010678306A CN111898253B CN 111898253 B CN111898253 B CN 111898253B CN 202010678306 A CN202010678306 A CN 202010678306A CN 111898253 B CN111898253 B CN 111898253B
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付湘
刘双郡
秦嘉楠
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Abstract

The invention discloses a cooperative value evaluation method for reservoir scheduling and downstream river ecological environment protection, wherein an initial method is improved by using an Eckhardt filtering technology before reservoir ecological scheduling is carried out, and a daily scale ecological base flow is generated; the power generation benefit and the ecological index of the hydropower station are taken as different targets, and a multi-target optimization model comprehensively considering the power generation, ecology and shipping processes is established; meanwhile, a cooperation mode between a hydropower station power generation target and an ecological target is considered, and a cooperation scheduling scene of two main alliances is established; and evaluating the ecological value of the downstream river channel by the complex reservoir system for cooperative ecological scheduling through the optimization calculation results among different scenes and the cooperative ecological value under different weight conditions. According to the method, the ecological value of the downstream river channel is evaluated by performing cooperative ecological scheduling on the complex reservoir system, a cooperative value evaluation model is established, and a multi-dimensional theoretical basis is provided for reservoir scheduling.

Description

Reservoir dispatching and downstream river ecological environment protection cooperation value evaluation method
Technical Field
The invention belongs to the field of reservoir optimization scheduling, relates to a set of ecological cooperation value evaluation system, and particularly relates to a cooperation value evaluation method for reservoir scheduling and ecological environment protection of downstream rivers.
Background
The reservoir system is changeable, is a complex system with multiple targets and multiple main bodies, and has the functions of flood control, power generation, ecology, shipping, water supply and the like. These goals are in conflicting and supporting relationship. Now, the ecological value of the river and lake system is more and more emphasized, but an evaluation standard which is commonly used in the industry is not generated yet. On the basis that the ecological value of rivers is not completely evaluated, ecological scheduling schemes with different standards are vigorously carried out by nationwide reservoirs. Ecological dispatching means that a reservoir specially keeps a certain flow discharged during dispatching to maintain the ecological environment inside and outside a river channel, and the dispatching mode influences the power generation benefit of a hydropower station. Wherein, the power generation benefit can be directly monetized, while the ecological benefit is not. Therefore, if a decision maker wants to comprehensively consider an optimal scheduling scheme with ecological objectives, the real monetary benefits brought by ecological scheduling must be known.
Generally, when solving a multi-objective problem, a solution set is a set of solutions with preferences. In the non-inferior solution, the benefit of one objective function must be improved at the expense of the benefit of other objective functions. So by borrowing the notion of a trade-off strategy, the deterioration of one objective can be compensated by the lifting of another objective. In view of the above, the invention provides an ecological value evaluation method for reservoir cooperation scheduling with ecological scheduling as background, which quantifies and increases ecological economic impact brought by power generation benefit by reducing comprehensive ecological indexes, thereby defining and monetizing the ecological cooperation value in the reservoir scheduling process.
Disclosure of Invention
The invention aims to evaluate the cooperative value in a reservoir cooperative ecological scheduling scheme, and concretize the ecological value of cooperative scheduling by using the fluctuation of the generated energy, so as to discuss the cooperative value and the possibility of the reservoir and realize the unified management of water resources and the evaluation of the cooperative value.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a cooperative value evaluation method for reservoir scheduling and downstream river ecological environment protection is characterized by comprising the following steps:
step 1: and (3) identifying the ecological base flow, wherein the formula (2) is obtained based on Eckhardt double-parameter filtering method ecological base flow identification:
Figure GDA0003461782570000011
wherein, btIs the ecological base flow at time t, ytIs total radial flow at time t, a is a base flow backscattering coefficient, is an empirical value constant, BFImaxThe maximum base flow index of the control station for many years, and t is time;
step 2: modeling a single-library multi-objective optimization model, wherein the establishment of the multi-objective optimization model mainly comprises the establishment of an objective function and the abstraction of constraint conditions;
step 2.1, the objective functions comprise a power generation benefit objective function of the hydropower station and an ecological environment benefit objective function of the hydropower station, and are respectively as follows:
generating benefit objective function:
Figure GDA0003461782570000021
wherein HP is total power generation, eta is efficiency coefficient, RGiThe flow rate for hydroelectric power generation on day i, g is the acceleration of gravity,
Figure GDA0003461782570000022
average reservoir level, h, day idown,iThe downstream tail water level of the hydropower station on the ith day;
ecological environment benefit objective function:
Figure GDA0003461782570000023
wherein EI is the overall ecological index, P is the total number of ecological indexes, wpIs the weight of the p-th ecological index, Ar,pIs the index value of the p-th ecological index after reservoir operation, An,pIs the index value of the p-th ecological index in a natural state;
step 2.2, setting main constraint conditions of reservoir operation, namely water balance constraint, reservoir storage level constraint, reservoir lower discharge flow amplitude constraint, hydropower station output constraint, power station output amplitude constraint and shipping flow constraint;
and 4, step 4: scheduling scenario design
The method analyzes the cooperation value of reservoir ecological scheduling, so that the power generation benefit and the ecological environment benefit of the reservoir are taken as two main bodies to be applied to game theory analysis; at this time, the non-cooperative situation in the game theory analysis is changed into a single-target optimization situation for reservoir scheduling, and the cooperative situation is analogized to a multi-target optimization scheduling situation in the reservoir scheduling; therefore, 3 scenes are totally counted in the analysis process, the scene one and the scene two are single-target optimization problems, represent a non-cooperative state in game theory analysis, and can determine the upper boundary and the lower boundary of two target functions; scenario three should be a cooperative state, which is a multi-objective optimization problem; in order to solve the multi-target optimization problem, HP and EI are combined together through the weight lambda, the complex multi-target problem is changed into a single-target optimization problem, and therefore the ecological cooperation value of reservoir scheduling can be obtained through comparison between a cooperation state and a non-cooperation state and further game equilibrium analysis;
and 5: collaborative value assessment
In the optimization process, the emphasis on ecological indexes can reduce the generating space of the hydropower station and influence the final generating capacity. In a trade-off strategy, an improvement in one objective can be compensated by a deterioration in another objective; based on the thought, the scheduling result under the condition of the detailed analysis scene three conditions provides a method for specifically measuring the downstream power generation influence brought by reservoir ecological scheduling by using the water and electricity income; the pareto optimal concept guarantees an optimal solution S relative to only considering the power generation benefitsHRelative to the optimal solution S under other different conditionsλIs compensated by a reduction in hydroelectric yield; that means that the reduction in the hydroelectric revenue can be used to characterize the ecological value of the collaborative scheme; specifically, the optimal solution S under the condition of the weight λλCooperative ecological value v ofλCan be defined as:
Figure GDA0003461782570000031
wherein HP (S)H) Indicates the total power generation amount, EI (S), considering only the power generation benefitH) Represents a comprehensive ecological index, HP (S), only considering the power generation benefit conditionλ) Represents the optimal total power generation amount, EI (S) under the condition that the power generation benefit weight is lambdaλ) And (3) representing the optimal comprehensive ecological index under the condition that the power generation benefit weight is lambda, and setting a fixed electricity price for the model by the EP.
Further, in step 1, the specific steps of ecological base flow identification are as follows:
step 1.1, annual flow process of hydrologic control stationDividing the data into m segments according to a given time interval N, and determining the minimum flow sequence (q) of each segment1,q2,...,qm) (ii) a Then determining an inflection point therefrom; then all the inflection points are connected to obtain a base flow process, and the base flow between the inflection points is obtained by linear interpolation;
step 1.2, calculating the base flow index value through the following formula (1) after the base flow process is obtained
Figure GDA0003461782570000032
Wherein Q isBasicIs the total flow rate of the base flow process, QTotalIs the total runoff, BFI is the base flow index;
and 1.3, calculating the ecological base flow at different moments according to the base flow index and a formula (2).
Further, in step 1, the method for determining an inflection point in the minimum flow sequence of each segment specifically includes: starting from the second sequence in the minimum flow rate sequence, multiplying the sequence by a coefficient k, and judging if k × q is satisfiedtLess than qt-1And q ist+1Then q istK is an inflection point, is an artificially defined control base flow precision parameter, is a constant and has a value range of 0.9 to 0.979.
Further, in step 2.1, the ecological index is evaluated by adopting a hydrologic change index-based method, and the index system comprises 5 characteristics of reflecting the flow rate, frequency, duration, period and change rate.
Further, in step 2.1, the ecological indexes include 32 flow indexes classified into 5 categories, specifically as follows:
the first type is monthly average flow, which has 12 indexes;
Figure GDA0003461782570000041
wherein A ismIs the monthly mean flow in the m month, JmIs the total number of days of the m month, QiIs the m < th > oneRunoff on day i of the month;
the second type is annual extreme flow, which is 12 indexes in total;
at a maximum of 1 day flux A13And a minimum 1 day flow rate A18For example, the calculation formula is as follows:
Figure GDA0003461782570000042
Figure GDA0003461782570000043
wherein the subscript i of the summation symbol indicates day i of the year, and the superscript i + x-1 is the upper bound of the summation in equations (6) and (7), for a maximum day 1 flux A13And a minimum 1 day flow rate A18X is 1; qiIs the runoff on day i; similarly, x in the formula (6) is respectively replaced by 3, 7, 30 and 90 to obtain the maximum 3-day flow A14Maximum 7 day flow rate A15Maximum 30 day flow rate A16And a maximum flow rate of 90 days A17(ii) a Similarly, x in the formula (7) is respectively replaced by 3, 7, 30 and 90 to obtain the minimum 3-day flow A19Minimum 7 day flow rate A20Minimum 30 day flow rate A21And minimum 90 day flow rate A22；
In the IHA system, a mean flow index is defined by A23To represent;
Figure GDA0003461782570000044
wherein A is20A minimum annual flow rate of 7 days, IiThe water amount in the ith period;
the third type is the time of annual extreme flow, which is 2 indexes in total;
Figure GDA0003461782570000045
Figure GDA0003461782570000046
Figure GDA0003461782570000047
Figure GDA0003461782570000051
wherein A is24Time of year maximum of Roman diary, A25Time of appearance at annual minimum of Roman diary, C1Is a minuscule constant for ensuring the rationality of the equation;
the fourth type is the frequency and duration of high and low flow, which are 4 indexes;
bounded by daily flows with overrun probabilities of 25% and 75%, A26Frequency of high flow, A27For the duration of high flow, A28At a low flow rate frequency, A29Duration of low flow;
the fifth category is 3 indicators of the rate of change and frequency of water flow conditions.
Figure GDA0003461782570000052
Figure GDA0003461782570000053
Figure GDA0003461782570000054
Figure GDA0003461782570000055
Figure GDA0003461782570000056
Figure GDA0003461782570000057
Wherein, DSiIs the amplitude of the bleed-down flow, RiseNum is the number of continuous rising events, FallNum is the number of continuous falling events, C2A minimum constant to ensure the rationality of the equation; a. the30For continuous daily flux increase, A31For successive daily reductions in flow, A32The total times of water falling in the year.
Further, in step 2.2, determining the shipping minimum flow as the shipping flow constraint according to the reservoir scheduling rules, wherein the rest constraint conditions are as follows:
2.2.1 Water balance constraints
Vt+1＝Vt+(It-Qt) Δ t equation (19)
Wherein, Vt、Vt+1Initial and final storage capacities, I, of the reservoir at the t-th time intervaltIs the warehousing traffic of the t-th time period, QtThe flow rate is the lower leakage flow rate in the t-th time period, and when the output in the time period is less than the maximum output corresponding to the unit section, the flow rate is equal to the power generation flow rate;
2.2.2 reservoir water level constraints
Figure GDA0003461782570000061
Wherein,Z tand
Figure GDA0003461782570000062
the lowest water level and the highest water level allowed by the reservoir in the t-th time period are respectively;
2.2.3 reservoir discharge restriction
Figure GDA0003461782570000063
Wherein,q tand
Figure GDA0003461782570000064
for minimum and maximum let-down flows, BF, allowed for hydropower stations during the t-th periodtThe ecological flow of the control station in the t-th time period;
2.2.4 Down discharge flow amplitude variation restraint
|Qt-Qt-1|≤ΔQmaxFormula (22)
Wherein, is Δ QmaxThe maximum amplitude of daily flow of the hydropower station is obtained;
2.2.5 hydropower station output constraints
Figure GDA0003461782570000065
Wherein,N tand
Figure GDA0003461782570000066
maximum and minimum output limits, N, for the hydropower station at the t-th time periodtActual output force in the t-th time period;
2.2.6 power station output amplitude variation restraint
|Nt-Nt-1|≤50％×NTotalFormula (24)
Wherein N isTotalThe installed capacity of the hydropower station.
Further, in step 4, in order to solve the multi-objective optimization problem, the formula for combining HP and EI into a single-objective optimization through the weight λ is as follows:
CI＝λHPn+ (1-lambda) EI formula (25)
Figure GDA0003461782570000067
Wherein CI is a combination index of single-target optimization, HPnTotal power generation for dimensionalization of the derogationAnd (4) indexes.
The invention has the beneficial effects that:
the invention starts from multi-target reservoir ecological scheduling and aims to explore the ecological cooperation value of a downstream river channel in a complex reservoir system. Before reservoir ecological scheduling, an initial method is improved by using an Eckhardt filtering technology, and a daily scale ecological base flow is generated; the power generation benefit and the ecological index of the hydropower station are different targets, and a multi-target optimization model comprehensively considering the power generation, ecology and shipping processes is established; meanwhile, a cooperation mode between a hydropower station power generation target and an ecological target is considered, and a cooperation scheduling scene of two main alliances is established; and evaluating the ecological value of the downstream river channel by the complex reservoir system for cooperative ecological scheduling through the optimization calculation results among different scenes and the cooperative ecological value under different weight conditions.
Drawings
Fig. 1 is a schematic diagram showing multi-objective optimization results and maximum and minimum ecological cooperation values of annual ecological scheduling in three representative typical years by taking three gorges reservoir ecological scheduling as an example.
FIG. 2 is a statistical comparison chart of ecological cooperation values of ecological dispatch in the three gorges reservoir according to the present invention.
Detailed Description
In order to make the implementation purpose, technical scheme and advantages of the present invention more clear, the technical scheme of the present invention will be described with reference to the embodiments of the present invention.
A cooperative value evaluation method for reservoir scheduling and downstream river ecological environment protection comprises the following steps:
step 1: eckhardt double-parameter filtering method ecological base flow identification
Firstly, dividing the annual flow process of a hydrologic control station into m segments according to a given time interval N, and determining the minimum flow sequence (q) of each segment1,q2,...,qm) (ii) a And then determines an inflection point therefrom (if k × q is satisfied)tLess than qt-1And q ist+1Then q istIs an inflection point); then all the inflection points are connected to obtain a base flow process, and the base flow between the inflection points is obtained by linear interpolation。
The coefficient k is a parameter for controlling the accuracy of the base stream, is an artificially defined parameter, and generally takes a value of 0.9 or 0.979 in the model.
After the course of the rough base flow is obtained, the corresponding BFI (base flow index) value is calculated,
Figure GDA0003461782570000071
wherein Q isBasicIs the total flow rate of the base flow process, QTotalIs the total runoff.
However, the obtained runoff process is rough, the pulse process in natural flow cannot be grasped, and meanwhile, the runoff process is a basic flow process of ten-day scale, so that the problem can be solved to a certain extent by adopting an Eckhardt double-parameter filtering method on the basis:
Figure GDA0003461782570000072
wherein, btIs the ecological base flow at time t, ytIs total radial flow at time t, a is base flow backscattering coefficient, BFImaxT is the time for the control station to have a maximum base flow index for many years. The value of the fundamental flow backscattering coefficient a is usually 0.925-0.95, which is obtained according to experience.
Step 2: modeling of single-library multi-objective optimization model
The establishment of the multi-objective optimization model mainly comprises the establishment of an objective function and the abstraction of constraint conditions. In the invention, the power generation benefit of the hydropower station with the primary objective function is as follows:
Figure GDA0003461782570000081
wherein HP is total power generation, eta is efficiency coefficient, RGiThe flow rate for hydroelectric power generation on day i, g is the acceleration of gravity,
Figure GDA0003461782570000082
average reservoir level, h, day idown,iThe water level of the tail water at the downstream of the hydropower station on the ith day.
The hydropower station ecological environment benefit objective function is defined as follows:
Figure GDA0003461782570000083
wherein EI is the overall ecological index, P is the total number of ecological indexes, wpIs the weight of the p-th ecological index, Ar,pIs the index value of the p-th ecological index after reservoir operation, An,pIs the index value of the p-th ecological index in the natural state.
In the present invention, the ecological impact of reservoir operation was evaluated based on the hydrological change indicator method (IHA). The index system mainly comprises 32 flow indexes which can be divided into 5 categories, and the 5 characteristics of the flow, such as the size, the frequency, the duration, the period and the change rate, are reflected.
The first category is monthly average traffic, which is 12 indicators in total.
Figure GDA0003461782570000084
Wherein A ismIs the monthly mean flow in the m month, JmIs the total number of days of the m month, QiIs the runoff on day i of month m.
The second type is annual extreme flow, which is 12 indexes in total.
At a maximum of 1 day flux (A)13) And minimum 1 day flow (A)18) For example, the calculation formula is as follows:
Figure GDA0003461782570000085
Figure GDA0003461782570000091
wherein the subscript i of the summation symbol indicates day i of the year, and the superscript i + x-1 is the upper bound of the summation in equations (6) and (7), for a maximum day 1 flux A13And a minimum 1 day flow rate A18X is 1; qiIs the runoff on day i; similarly, x in the formula (6) is respectively replaced by 3, 7, 30 and 90, and the maximum 3-day flow A is obtained14Maximum 7 day flow rate A15Maximum 30 day flow rate A16And a maximum flow rate of 90 days A17(ii) a Similarly, x in the formula (7) is respectively replaced by 3, 7, 30 and 90 to obtain the minimum 3-day flow A19Minimum 7 day flow rate A20Minimum 30 day flow rate A21And minimum 90 day flow rate A22。
In particular, in the IHA system, a mean flow index is defined, and A is used23To indicate.
Figure GDA0003461782570000092
Wherein A is20A minimum annual flow rate of 7 days, IiIs the amount of water coming from the i-th period.
The third type is the time of annual extreme flow occurrence, which is 2 indexes in total.
Figure GDA0003461782570000093
Figure GDA0003461782570000094
Figure GDA0003461782570000095
Figure GDA0003461782570000096
Wherein A is24The first yearTime of occurrence of large value (in Roman diary), A25Time of appearance for minimum of year (in Roman diary), C1Is a minimal constant for ensuring the rationality of the equation.
The fourth type is the frequency and duration of high and low flows, which are 4 indicators in total.
Bounded by daily flows with overrun probabilities of 25% and 75%, A26Frequency of high flow, A27For the duration of high flow, A28At a low flow rate frequency, A29The duration of the low flow.
The fifth category is 3 indicators of the rate of change and frequency of water flow conditions.
Figure GDA0003461782570000101
Figure GDA0003461782570000102
Figure GDA0003461782570000103
Figure GDA0003461782570000104
Figure GDA0003461782570000105
Figure GDA0003461782570000106
Wherein, DSiIs the amplitude of the downward discharge flow, RiseNum and FallNum are the times of continuous rising and continuous falling events, C2Is a very small constant to ensure the rationality of the formula. A. the30For continuous daily flux increase, A31For successive daily reductions in flow, A32The total times of water falling in the year.
Then, setting main constraint conditions of reservoir operation, namely water balance constraint, reservoir water level constraint, reservoir lower discharge flow amplitude constraint, hydropower station output amplitude constraint, power station output amplitude constraint and shipping flow constraint, wherein the lowest shipping flow can be determined as the shipping flow constraint according to reservoir scheduling rules, and the rest constraint conditions are as follows:
(1) water balance constraint
Vt+1＝Vt+(It-Qt) Δ t equation (19)
Wherein, Vt、Vt+1Initial and final storage capacities, I, of the reservoir at the t-th time intervaltIs the warehousing traffic of the t-th time period, QtAnd the flow rate is the lower leakage flow rate in the t-th time period, and when the output in the time period is less than the maximum output corresponding to the unit section, the flow rate is equal to the power generation flow rate.
(2) Reservoir water level restraint
Figure GDA0003461782570000107
Wherein,Z tand
Figure GDA0003461782570000108
the lowest and highest water levels allowed for the reservoir during the t-th period.
(3) Reservoir discharge restriction
Figure GDA0003461782570000111
Wherein,q tand
Figure GDA0003461782570000112
for minimum and maximum let-down flows, BF, allowed for hydropower stations during the t-th periodtThe ecological flow of the control site in the t-th time period.
(4) Downward discharge flow amplitude variation restriction
|Qt-Qt-1|≤ΔQmaxFormula (22)
Wherein, is Δ QmaxThe maximum amplitude of daily flow of the hydropower station is obtained.
(5) Hydropower station output restriction
Figure GDA0003461782570000113
Wherein,N tand
Figure GDA0003461782570000114
maximum and minimum output limits, N, for the hydropower station at the t-th time periodtIs the actual force applied during the t-th period.
(6) Power station output amplitude-variation restraint
|Nt-Nt-1|≤50％×NTotalFormula (24)
Wherein N isTotalThe installed capacity of the hydropower station.
And 4, step 4: scheduling scenario design
The invention analyzes the cooperation value of reservoir ecological scheduling, so the power generation benefit and the ecological benefit of the reservoir are taken as two main bodies, and the game theory analysis is conveniently applied. At this time, the non-cooperative scenario in the game theory analysis is changed into a single-target optimization scenario for reservoir scheduling, and the cooperative scenario is analogized to a multi-target optimization scheduling scenario in the reservoir scheduling. Therefore, 3 scenes are counted in the analysis process. The first scenario and the second scenario are single target optimization problems, represent non-cooperative states in game theory analysis, and can determine the upper boundary and the lower boundary of two objective functions. Scenario three should be a cooperative state, which is a multi-objective optimization problem. In order to solve the multi-objective optimization problem, HP and EI are combined together through a weight lambda, and the complex multi-objective problem is changed into a single-objective optimization problem. Therefore, the ecological cooperation value of reservoir scheduling can be obtained through comparison between the cooperation state and the non-cooperation state and further game balance analysis.
In order to coordinate the unit conversion relationship between the two targets, the HP is subjected to de-dimension by using a standardization method.
CI＝λHPn+ (1-lambda) EI formula (25)
Figure GDA0003461782570000115
Wherein CI is a combination index of single-target optimization, HPnIs the total power generation index of the descaler dimensionalization.
Table 1 shows a cooperation status table for three scenarios
Figure GDA0003461782570000121
And 5: collaborative value assessment
In the optimization process, the improvement of ecological indexes leads to the reduction of the power generation decision space of the hydropower station, and finally influences the total generated energy. By analogy, in a trade-off strategy, an improvement of one target can be compensated by a deterioration of another target. Based on the thought, the scheduling result under the situation three is analyzed in detail, and a method for specifically measuring downstream ecological influence brought by reservoir ecological scheduling by using hydropower income is provided. The pareto optimal concept may guarantee an optimal solution S relative to only considering power generation benefitsHRelative to the optimal solution S under other different conditionsλIs compensated by a reduction in the hydroelectric yield. That means that this reduction can be used to characterize the ecological value of the collaborative scenario. Specifically, the optimal solution S under the condition of the weight λλCooperative ecological value v ofλCan be defined as:
Figure GDA0003461782570000122
wherein HP (S)H) And EI (S)H) Indicates that the power generation amount and the ecological index, HP (S) are only considered under the condition of power generation benefitλ) And EI (S)λ) Represents the weight of the power generation benefitFor optimal power generation and ecological index under lambda conditions, EP sets a fixed electricity price for the model.
The invention is mainly applied to the field of reservoir ecological scheduling, and explores a method for quantifying the ecological cooperation value in a complex reservoir system.
In view of this, taking the three gorges reservoir as an example, different weight optimization scheduling calculations of power generation and downstream river ecological targets under cooperative conditions are performed, the implementation process of the invention is simple and clear, and the key results are as follows:
in the example, the electricity price refers to the electricity price of industrial and commercial use in Yichang city and other uses, recorded as 0.6507 yuan/kilowatt hour.
TABLE 2 statistics table for ecological cooperation value of three gorges scheduling
Figure GDA0003461782570000123
Figure GDA0003461782570000131
From the cooperation value evaluation results in table 1, the ecological cooperation values in the dry year, the open year and the rich year are higher and higher, and the effectiveness and significance of ecological cooperation scheduling are proved. In the dry year and the rich year, the scope of the decision scheme is reduced due to the limitation of various constraint conditions. The cooperation value between the dry and flat years does not change in the form of cliff. Such results can provide data support to decision makers.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but other embodiments derived from the technical solutions of the present invention by those skilled in the art are also within the scope of the present invention.

Claims (7)

1. A cooperative value evaluation method for reservoir scheduling and downstream river ecological environment protection is characterized by comprising the following steps:
step 1: and (3) identifying the ecological base flow, wherein the formula (2) is obtained based on Eckhardt double-parameter filtering method ecological base flow identification:
Figure FDA0003461782560000011
wherein, btIs the ecological base flow at time t, ytIs total radial flow at time t, a is a base flow backscattering coefficient, is an empirical value constant, BFImaxThe maximum base flow index of the control station for many years, and t is time;
step 2: modeling a single-library multi-objective optimization model, wherein the building of the multi-objective optimization model comprises the building of an objective function and the abstraction of constraint conditions;
step 2.1, the objective functions comprise a power generation benefit objective function of the hydropower station and an ecological environment benefit objective function of the hydropower station, and are respectively as follows:
generating benefit objective function:
Figure FDA0003461782560000012
wherein HP is total power generation, eta is efficiency coefficient, RGiThe flow rate for hydroelectric power generation on day i, g is the acceleration of gravity,
Figure FDA0003461782560000013
average reservoir level, h, day idown,iThe downstream tail water level of the hydropower station on the ith day;
ecological environment benefit objective function:
Figure FDA0003461782560000014
wherein EI is the overall ecological index, P is the total number of ecological indexes, wpIs the weight of the p-th ecological index, Ar,pIs the p-th one after the operation of the reservoirIndex value of the ecological index, An,pIs the index value of the p-th ecological index in a natural state;
step 2.2, setting constraint conditions of reservoir operation, namely water balance constraint, reservoir water storage level constraint, reservoir lower discharge flow amplitude constraint, hydropower station output constraint, power station output amplitude constraint and shipping flow constraint;
and 4, step 4: scheduling scenario design
In order to analyze the cooperation value of reservoir ecological scheduling, the power generation benefit and the ecological environment benefit of the reservoir are taken as two main bodies to be applied to game theory analysis; at this time, the non-cooperative situation in the game theory analysis is changed into a single-target optimization situation for reservoir scheduling, and the cooperative situation is analogized to a multi-target optimization scheduling situation in the reservoir scheduling; therefore, 3 scenes are totally counted in the analysis process, the scene one and the scene two are single-target optimization problems, represent a non-cooperative state in game theory analysis, and can determine the upper boundary and the lower boundary of two target functions; scenario three should be a cooperative state, which is a multi-objective optimization problem; in order to solve the multi-target optimization problem, HP and EI are combined together through the weight lambda, the complex multi-target problem is changed into a single-target optimization problem, and therefore the ecological cooperation value of reservoir scheduling can be obtained through comparison between a cooperation state and a non-cooperation state and further game equilibrium analysis;
and 5: collaborative value assessment
In the optimization process, the improvement of ecological indexes leads to the reduction of the power generation decision space of the hydropower station, and the total power generation is finally influenced, so that the improvement of one target can be compensated by the deterioration of the other target in a balancing strategy; based on the thought, the scheduling result under the situation three is analyzed in detail, and a method for specifically measuring the downstream ecological influence brought by reservoir ecological scheduling by using the hydropower income is provided; the pareto optimal concept may guarantee an optimal solution S relative to only considering power generation benefitsHRelative to the optimal solution S under other different conditionsλIs compensated by a reduction in hydroelectric yield; that means a reduction in the hydroelectric yieldThe quantities may be used to characterize the ecological value of the collaborative scheme; specifically, the optimal solution S under the condition of the weight λλCooperative ecological value v ofλCan be defined as:
Figure FDA0003461782560000021
wherein HP (S)H) Indicates the total power generation amount, EI (S), considering only the power generation benefitH) Represents a comprehensive ecological index, HP (S), only considering the power generation benefit conditionλ) Represents the optimal total power generation amount, EI (S) under the condition that the power generation benefit weight is lambdaλ) And (3) representing the optimal comprehensive ecological index under the condition that the power generation benefit weight is lambda, and setting a fixed electricity price for the model by the EP.
2. The method of claim 1, wherein the method comprises the following steps: in the step 1, the specific steps of ecological base flow identification are as follows:
step 1.1, dividing the annual flow process of the hydrologic control station into m sections according to a given time interval N, and determining the minimum flow sequence (q) of each section1,q2,...,qm) (ii) a Then determining an inflection point therefrom; then all the inflection points are connected to obtain a base flow process, and the base flow between the inflection points is obtained by linear interpolation;
step 1.2, calculating the base flow index value through the following formula (1) after the base flow process is obtained
Figure FDA0003461782560000022
Wherein Q isBasicIs the total flow rate of the base flow process, QTotalIs the total runoff, BFI is the base flow index;
and 1.3, calculating the ecological base flow at different moments according to the base flow index and a formula (2).
3. As claimed in claim 2The method for evaluating the cooperation value of reservoir scheduling and downstream river ecological environment protection is characterized by comprising the following steps of: in step 1, the method for determining the inflection point in the minimum flow sequence of each segment specifically comprises the following steps: starting from the second sequence in the minimum flow rate sequence, multiplying the sequence by a coefficient k, and judging if k × q is satisfiedtLess than qt-1And q ist+1Then q istK is an inflection point, is an artificially defined control base flow precision parameter, is a constant and has a value range of 0.9 to 0.979.
4. The method of claim 1, wherein the method comprises the following steps: in step 2.1, the ecological indexes adopt a hydrologic change index method to evaluate the ecological influence of reservoir operation, and the index system comprises 5 characteristics of reflecting flow rate, frequency, duration, period and change rate.
5. The method of claim 4, wherein the method comprises the following steps: in step 2.1, the ecological indexes include 32 flow indexes classified into 5 categories, specifically as follows:
the first type is monthly average flow, which has 12 indexes;
Figure FDA0003461782560000031
wherein A ismIs the monthly mean flow in the m month, JmIs the total number of days of the m month, QiIs the runoff on day i of month m;
the second type is annual extreme flow, which is 12 indexes in total;
at a maximum of 1 day flux A13And a minimum 1 day flow rate A18For example, the calculation formula is as follows:
Figure FDA0003461782560000032
Figure FDA0003461782560000033
wherein the subscript i of the summation symbol indicates day i of the year, and the superscript i + x-1 is the upper bound of the summation in equations (6) and (7), for a maximum day 1 flux A13And a minimum 1 day flow rate A18X is 1; qiIs the runoff on day i; similarly, x in the formula (6) is respectively replaced by 3, 7, 30 and 90 to obtain the maximum 3-day flow A14Maximum 7 day flow rate A15Maximum 30 day flow rate A16And a maximum flow rate of 90 days A17(ii) a Similarly, x in the formula (7) is respectively replaced by 3, 7, 30 and 90 to obtain the minimum 3-day flow A19Minimum 7 day flow rate A20Minimum 30 day flow rate A21And minimum 90 day flow rate A22；
In the IHA system, a mean flow index is defined by A23To represent;
Figure FDA0003461782560000041
wherein A is20A minimum annual flow rate of 7 days, IiThe water amount in the ith period;
the third type is the time of annual extreme flow, which is 2 indexes in total;
Figure FDA0003461782560000042
Figure FDA0003461782560000043
Figure FDA0003461782560000044
Figure FDA0003461782560000045
wherein A is24Time of year maximum of Roman diary, A25Time of appearance at annual minimum of Roman diary, C1Is a minuscule constant for ensuring the rationality of the equation;
the fourth type is the frequency and duration of high and low flow, which are 4 indexes;
bounded by daily flows with overrun probabilities of 25% and 75%, A26Frequency of high flow, A27For the duration of high flow, A28At a low flow rate frequency, A29Duration of low flow;
the fifth type is the change rate and frequency of the water flow conditions, and 3 indexes are provided;
Figure FDA0003461782560000046
Figure FDA0003461782560000047
Figure FDA0003461782560000048
Figure FDA0003461782560000049
Figure FDA0003461782560000051
Figure FDA0003461782560000052
wherein, DSiIs the amplitude of the bleed-down flow, RiseNum is the number of continuous rising events, FallNum is the number of continuous falling events, C2A minimum constant to ensure the rationality of the equation; a. the30For continuous daily flux increase, A31For successive daily reductions in flow, A32The total times of water falling in the year.
6. The method of claim 5, wherein the method comprises the following steps: in step 2.2, determining the shipping minimum flow as the shipping flow constraint according to the reservoir scheduling regulation, wherein the rest constraint conditions are as follows:
2.2.1 Water balance constraints
Vt+1＝Vt+(It-Qt) Δ t equation (19)
Wherein, Vt、Vt+1Initial and final storage capacities, I, of the reservoir at the t-th time intervaltIs the warehousing traffic of the t-th time period, QtThe flow rate is the lower leakage flow rate in the t-th time period, and when the output in the time period is less than the maximum output corresponding to the unit section, the flow rate is equal to the power generation flow rate;
2.2.2 reservoir water level constraints
Figure FDA0003461782560000053
Wherein,Z tand
Figure FDA0003461782560000054
the lowest water level and the highest water level allowed by the reservoir in the t-th time period are respectively;
2.2.3 reservoir discharge restriction
Figure FDA0003461782560000055
Wherein,q tand
Figure FDA0003461782560000056
for minimum and maximum let-down flows, BF, allowed for hydropower stations during the t-th periodtThe ecological flow of the control station in the t-th time period;
2.2.4 Down discharge flow amplitude variation restraint
|Qt-Qt-1|≤ΔQmaxFormula (22)
Wherein, is Δ QmaxThe maximum amplitude of daily flow of the hydropower station is obtained;
2.2.5 hydropower station output constraints
Figure FDA0003461782560000057
Wherein N istAnd
Figure FDA0003461782560000061
maximum and minimum output limits, N, for the hydropower station at the t-th time periodtActual output force in the t-th time period;
2.2.6 power station output amplitude variation restraint
|Nt-Nt-1|≤50％×NTotalFormula (24)
Wherein N isTotalThe installed capacity of the hydropower station.
7. The method of claim 5, wherein the method comprises the following steps: in step 4, in order to solve the multi-objective optimization problem, HP and EI are combined together by the weight λ to become a single-objective optimization formula as follows:
CI＝λHPn+ (1-lambda) EI formula (25)
Figure FDA0003461782560000062
Wherein CI is a combination index of single-target optimization, HPnIs the total power generation index of the descaler dimensionalization.
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